Assessing the Similarity between Random Copolymer Drug Glatiramer Acetate by Using LC-MS Data Coupling with Hypothesis Testing

Hsin Yi Wu, Mi Chia Ma, Yu Yi Pan, Chia Lung Shih, Victor Zgoda, Chin Shang Li, Lung Cheng Lin, Pao Chi Liao

Research output: Contribution to journalArticle

Abstract

The full characterization of nonbiological complex drugs (NBCDs) is not possible, but analytical approaches are of urgent need to evaluate the similarity between different lots and compare with their follow-up versions. Here, we propose a hypothesis testing-based approach to assess the similarity/difference between random amino acid copolymer drugs using liquid chromatography mass spectrometry (LC-MS) analysis. Two glatiramer acetate (GA) drugs, commercially available Copaxone and in-house synthesized SPT, and a negative control were digested by Lys-C and followed by HILIC-MS analysis. After retention time alignment and feature identification, 1627 features matched to m/z values in an elemental composition database were considered as derived from active drug ingredients. A hypothesis testing approach, the sum of squared deviations test, was developed to process high-dimensional data derived from LC-MS spectra. The feasibility of this approach was first demonstrated by testing 5 versus 5 lots of Copaxone and Copaxone versus SPT, which suggested a significant similarity by obtaining the estimated 95th percentile of the distribution of the estimator (ρ'(95%)) at 0.0056 (p-value = 0.0026) and 0.0026 (p-value < 0.0001), respectively. In contrast, the ρ' was 0.036 (p-value = 1.00), while comparing Copaxone and the negative control, implying a lack of similarity. We further synthesized nine stable isotope-labeled peptides to validate the proposed amino acid sequences in the database, demonstrating the correctness in sequence identification. The quantitation variations in our analytical procedures were determined to be 6.8-7.7%. This approach was found to have a great potential for evaluating the similarity between generic NBCDs and listed reference drugs, as well as to monitor the lot-to-lot variation.

Original languageEnglish
Pages (from-to)14281-14289
Number of pages9
JournalAnalytical chemistry
Volume91
Issue number22
DOIs
Publication statusPublished - 2019 Nov 19

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry

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